genpark-agent-self-consistency-consensus-scorer-skill
OfficialClick on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@genpark-agent-self-consistency-consensus-scorer-skillrun self-consistency consensus scoring on these 5 reasoning paths for the math problem"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-agent-self-consistency-consensus-scorer-skill
🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation
📌 Overview & Capability
genpark-agent-self-consistency-consensus-scorer-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server designed for autonomous AI agents, multi-agent frameworks (LangGraph, CrewAI, AutoGen, OpenAI Swarm), and developer environments (Cursor, Windsurf, Claude Desktop).
Executive Capability: Multi-path chain-of-thought self-consistency consensus scorer and majority voter aggregating divergent LLM reasoning trajectories into high-confidence deterministic outputs.
⚡ Key Highlights
🐍 Zero External
pipDependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.
⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.
🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.
Related MCP server: moa-mcp
🏗️ Architecture
graph LR
Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-agent-self-consistency-consensus-scorer-skill Server]
Server --> Core[🧠 Deterministic Processing Core]
Core --> Out[📊 Actionable Result & Telemetry]
Out --> Agent🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import AgentSelfConsistencyConsensusScorer
client = AgentSelfConsistencyConsensusScorer()
result = client.run_consensus_benchmark()
print(result)🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-agent-self-consistency-consensus-scorer-skill": {
"command": "python",
"args": ["/path/to/genpark-agent-self-consistency-consensus-scorer-skill/mcp_server.py"]
}
}
}Direct MCP Testing
python mcp_server.py --test📊 Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary context, code, schema, or content input |
|
| No | Execution flags, compression ratios, or risk bounds |
This server cannot be deployed
Maintenance
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